Learning from big data

Learn how to formalize marketing problems as statistical models and how to solve marketing problems by complementing econometric techniques with modern machine learning (ML) methods.

Every day, millions of consumers voice their opinions in product-review websites, blogs and chat rooms. They produce massive amounts of user-generated content (UGC), most of which is textual and freely available for analysis. At the same time, online and offline retailers collect rich data sets that contain valuable information about the purchases of individual customers. In this era of big data, the availability of larger and more diversified data sets opens up exciting opportunities for marketing researchers and practitioners.
However, the characteristics of big data (volume, velocity, variety) also pose challenges for modelers. With so much data available, it can be hard to determine what information is correct or how to separate signal from noise. For example, how can you extract information about market structure and consumer preferences from purchase data? To make this more difficult, UGC data is often unstructured and textual. Do you know how to analyze this data? And how do you go from insights to marketing decisions?
All that is needed to address these challenges is the right set of tools and the training that helps you to use these tools correctly. This course first teaches how to formalize marketing problems as statistical models. It then shows how to solve marketing problems by complementing econometric techniques with modern machine learning (ML) methods. You will find tools and conceptual frameworks needed to identify and exploit the opportunities that big data sources open up.
The course has three modules, each containing lectures and hands-on activities:

• Mine your Own Business in Blogs, Reviews and Tweets
• Purchase Prediction with Boosted Trees
• Neural Networks–From Market Structure Analysis to Prescriptive Marketing

• Formulate marketing decisions as machine learning tasks.
• Build well-structured and robust machine learning data/model pipelines.
• Extract relevant insights on consumer preferences and behavior from user-generated content (UGC).
• Analyze real-world loyalty card data to solve prescriptive marketing problems.
• Collaborate efficiently when working on data science projects in teams.
• Infer machine learning best practices from academic literature.

Teaching methods

• Lecture
• Tutorials
• Group assignment and individual assignments
• All RSM minors have mandatory attendance

Teaching materials
Details will be announced at the start of the course.

Method of examination

• One group assignment, in groups of 4 to 5 students
• Two individual assignments
• Participation in lectures (attendance, responses to cold calls, other contributions in class)

Composition final grade

• Group assignment (30%)
• Individual assignments (60%)
• Participation (10%)

This course relies a lot on statistical concepts and tools you are expected to apply independently (or study independently to catch up with gaps in your knowledge). Successful participation in at least one advanced statistics or data science courses is strongly recommended (e.g., Applied Statistics, Econometrics 1, Introduction to Multivariate Statistics). Also note that other data science, statistics, and computer science courses might cover more specific knowledge that can be useful for this course.

In the homework assignments, you will write your own machine learning pipelines (data transformation, model estimation, model validation), so you must be proficient in Python, R or a similar programming. The course material is written mostly in Python. Please keep in mind that this course will not teach you how to use Python or R, so please prepare in advance.